Firing Costs,Efficiency Wages and Unemployment

This paper uses a Shapiro–Stiglitz efficiency wage model to analyse the effects of firing costs on wages, employment, expected utility and profits. It considers that the probability of a non–shirker being fired depends on an exogenous shock which follows a two–state Markov process. It finds that higher severance payments give rise to lower wages, a lower unemployment rate, an increase in firms’ profits and a decrease in the utility of both workers and the unemployed. These conclusions derive from the finding that a greater probability of keeping one’s job, because of higher firing costs, raises the value of the job and reduces the worker’s incentives to behave opportunistically; this enables firms to reduce wages. Hence, if firms pay efficiency wages, a higher degree of labour market flexibility increases unemployment.     

Blur-generated non-separable space–time models

Statistical space–time modelling has traditionally been concerned with separable covariance functions, meaning that the covariance function is a product of a purely temporal function and a purely spatial function. We draw attention to a physical dispersion model which could model phenomena such as the spread of an air pollutant. We show that this model has a non-separable covariance function. The model is well suited to a wide range of realistic problems which will be poorly fitted by separable models. The model operates successively in time: the spatial field at time t +1 is obtained by 'blurring' the field at time t and adding a spatial random field. The model is first introduced at discrete time steps, and the limit is taken as the length of the time steps goes to 0. This gives a consistent continuous model with parameters that are interpretable in continuous space and independent of sampling intervals. Under certain conditions the blurring must be a Gaussian smoothing kernel. We also show that the model is generated by a stochastic differential equation which has been studied by several researchers previously.     

The more you know,the more you give: Influence of online disclosure on European community foundations' donations

Competition is high in the charitable contributions market, and donors demand to know how nonprofit organizations use the money they receive. In scrutinizing the variables that affect the capacity of nonprofits to attract donations, previous research has highlighted the positive influence of the amount of financial and performance information that nonprofits disclose through their websites. This study explored whether the depth of the organizations' online disclosures also affects these donations. In line with existing studies on regression‐based economic models of giving, this study considered community foundations—focusing on the United Kingdom and Italy—and its results indicated that managing the depth of the information provided through financial reports can influence donors' sensitivity and willingness to donate.     

Diversity patterns of alien and native plant species in Trieste port area: exploring the role of urban habitats in biodiversity conservation

Nowadays, urban areas play a crucial role in biodiversity conservation and habitat protection despite the constant pressures on which these habitats are subjected. They may even host relatively new plant communities due to the peculiar ecosystem where they vegetate. The port of Trieste (NE Italy) is characterized by a mixed mosaic of intensely human impacted areas (where commercial activities are still ongoing) flanked by abandoned areas where vegetation persists or has spontaneously recovered. In this study, we sampled the whole port area through a stratified random sampling by placing multiscalar nested plots in four different habitats (strata) previously identified by photo-interpretation. Plant species richness and abundance were assessed in each plot. Each species was then classified as native or alien and patterns of species richness and complementarity were compared among habitats. Results show that there is a significant difference in species richness patterns among habitats, while observed patterns are likely to vary at different spatial scales. As expected, urban plots account for most of the alien species in the sampling, while wooded plots cope better with invasion, accounting for a lower alien/native ratio. These results highlight how habitat diversity enhances biodiversity in urban areas and how it could provide an effective filtering effect able to reduce the spread of alien species. In addition, we provide further evidence for the use of multi-scale approaches in order to study the complex relationships between spatial heterogeneity and plant species richness.     

Unions and the Welfare Impact of Foreign Direct Investment — A Wisdom Extension

Abstract. This paper extends the recent literature, e.g. Leahy and Montagna (Economic Journal 110: 80–92, 2000), in relation to the link between unionization, inward foreign direct investment (FDI) and country welfare in an oligopoly market structure. It is shown that the common results, that unions may reduce welfare under FDI while multinational enterprises (MNEs) will strictly trade off union wages at each location, are generally driven by the assumption relating to the scope of the bargaining with the union, namely the ‘right to manage’ (RTM). In particular, our extension to efficient bargaining (EB) demonstrates that union power may increase welfare in the presence of FDI, while the MNEs’ choice between FDI and exports will include profit‐sharing arbitrage with unions, in addition to the usual wage comparison considerations.     

Cinderella purchasing transformation: linking purchasing status to purchasing practices and business performance

This paper contributes to the on-going debate about the relevance of the purchasing function for the firm value creation. We empirically examine the role of the purchasing function in improving business performance through an international survey based on 653 responses. Results suggest that purchasing practices related to spend rationalisation have a positive impact on the overall business performance, whereas supplier development & integration and sustainable purchasing have not. Moreover, the purchasing recognition by top managers and other organisational units emerge as a powerful antecedent of all purchasing practices. Instead, pure formal authority provided by the position in the organisation chart (i.e. the report level) does not have any influence on purchasing practices adoption.     

Identifying Environmental and Social Factors Predisposing to Pathological Gambling Combining Standard Logistic Regression and Logic Learning Machine

Identifying potential risk factors for problem gambling (PG) is of primary importance for planning preventive and therapeutic interventions. We illustrate a new approach based on the combination of standard logistic regression and an innovative method of supervised data mining (Logic Learning Machine or LLM). Data were taken from a pilot cross-sectional study to identify subjects with PG behaviour, assessed by two internationally validated scales (SOGS and Lie/Bet). Information was obtained from 251 gamblers recruited in six betting establishments. Data on socio-demographic characteristics, lifestyle and cognitive-related factors, and type, place and frequency of preferred gambling were obtained by a self-administered questionnaire. The following variables associated with PG were identified: instant gratification games, alcohol abuse, cognitive distortion, illegal behaviours and having started gambling with a relative or a friend. Furthermore, the combination of LLM and LR indicated the presence of two different types of PG, namely: (a) daily gamblers, more prone to illegal behaviour, with poor money management skills and who started gambling at an early age, and (b) non-daily gamblers, characterised by superstitious beliefs and a higher preference for immediate reward games. Finally, instant gratification games were strongly associated with the number of games usually played. Studies on gamblers habitually frequently betting shops are rare. The finding of different types of PG by habitual gamblers deserves further analysis in larger studies. Advanced data mining algorithms, like LLM, are powerful tools and potentially useful in identifying risk factors for PG.     

Everyday Life in Social Psychology

In the field of psychology, the topic of everyday life as a specific subject of inquiry has been afforded little attention. Indeed, everyday life has recently been analyzed mainly in psychological studies that examine people's ways of behaving and thinking when they act in situations termed as mundane and ordinary. These studies are mainly carried out in two fields of social psychology which we refer to in general terms as Social Cognition and Social Representation Theory. The aim of this paper is to examine how both these fields treat some of the features commonly attributed to everyday life. In particular, the features of familiarization, continuity and stability over time and automaticity are discussed in order to try to figure out meeting points between the two fields mentioned.     

Construction and Validation of the Moral Inclusion/Exclusion of Other Groups (MIEG) Scale

Moral exclusion is defined as viewing others as lying beyond the boundary within which moral values and rules of justice apply. In contrast, the process of moral inclusion involves the extension of social justice to several social groups. Thus, both moral inclusion and exclusion are a pivotal dimension in the study of social inequalities. Although the concept of moral inclusion/exclusion has a history of more than 20 years, research still lacks accurate instruments for measuring it. In this article, a first version of a scale that measures moral inclusion/exclusion was constructed and validated. The good reliability and correlation indexes found across the samples suggest that MIEG is a good measure for tapping into moral exclusion/inclusion symptoms.     

Moment Independent Importance Measures: New Results and Analytical Test Cases

Moment independent methods for the sensitivity analysis of model output are attracting growing attention among both academics and practitioners. However, the lack of benchmarks against which to compare numerical strategies forces one to rely on ad hoc experiments in estimating the sensitivity measures. This article introduces a methodology that allows one to obtain moment independent sensitivity measures analytically. We illustrate the procedure by implementing four test cases with different model structures and model input distributions. Numerical experiments are performed at increasing sample size to check convergence of the sensitivity estimates to the analytical values.